code: def indices_to_one_hot(data, nb_classes):
      """Convert an iterable of indices to one-hot encoded labels."""
      targets = np.array(data).reshape(-1)
      return np.eye(nb_classes)[targets]
  
file_path: SUOD/suod/models/parallel_processes.py
imports: from sklearn.base import clone
  import numpy
  from scipy.stats import rankdata
  from joblib import effective_n_jobs
  from sklearn.utils import check_array
  from sklearn.utils.validation import check_is_fitted
  from pyod.utils.utility import score_to_label
  from .jl_projection import jl_fit_transform, jl_transform
  from ..utils.utility import raw_score_to_proba
label_desc: Use image node_id 1 for calling a global function with example usage: indices_to_one_hot(data, nb_classes) and returns: np